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Chinese Control Conference (CCC)

作者： Jian Tang Meiying Jia Fupeng Wang Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology Beijing China Joint staff of the Central Military Commission 55th Institute Beijing China 91411 Unit PLA Dalian China College of computer and software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Mill load is one of the key factors that affects the operation optimization and control of grinding process. It is difficult to detect accurately in real time. The mill load relates to multiple channel mechanical signals, such as the mill shell vibration signals, the shaft seat front and rear vibration signals, the acoustic signals near to the shell surface and under the grinding area, etc. How to evaluate these sub-band feature of the mechanical spectrum of the above channels is a difficult problem. It can help to clarify the ball mill grinding mechanism and the mechanical signal generation mechanism. To solve these problems, this paper proposes a multi-channel mechanical frequency spectrum sub-band feature evaluation method based on multiple types correlation analysis. Firstly, the multi-channel mechanical signals are transformed from time domain to frequency domain to obtain the frequency spectrum data, which are divided into several sub-bands to extract features. Then, the normalized correlation coefficients between the multi-channel sub-band feature and the mill load are calculated. Thirdly, based on the partial least square algorithm, the variable projection importance (VIP) value of sub-band features are also calculated. The VIPs are normalized same as that of correlation coefficient. Finally, a new index is calculated by using normalized correlation coefficient and VIP value and the model's prediction performance. These sub-band features of single channel and all channel mechanical signals are measured based on the new defined combined evaluation index. The multi-channel mechanical signals of a laboratory-scale ball mill are used to verify the effectiveness of the proposed method.

关键词： Vibrations Feature extraction Correlation coefficient Shafts Acoustics Electronic mail Frequency-domain analysis

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Single-mode Sub-signal Selection Method Based on VMD and Predic Performance for Multi-component Mechanical Signal

Single-mode Sub-signal Selection Method Based on VMD and Pre...

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Chinese Control Conference (CCC)

作者： Jian Tang Hongjuan Zhu Dong Li Jian Zhang Gang Yu Faculty of Information Technology Beijing University of Technology Beijing China PLA Navy Beijing China 55th Institute Joint staff of the Central Military Commission Beijing China College of computer and software Nanjing University of Information Science and Technology Nanjing China State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition (VMD) and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill.

关键词： Vibrations Time-domain analysis Predictive models Time-frequency analysis Prediction algorithms Electronic mail Minerals

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Research and Application of Nelson Concentrator in Beneficiation Test of Quartz Vein Type Gold Ore

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IOP Conference Series: Earth and Environmental Science 2020年第5期546卷

作者： Wenping Zhang Mingming Cai Guangsheng Li Fabo Huang Xingfu Zhu Chao Xu Jianjun Zhao Department Metallurgical Laboratory Branch of Shandong Gold Mining Technology Co. Ltd Yantai 261441 Shandong China Department State Key Laboratory of Process Automation in Mining & Metallurgy

A gold deposit in Shandong belongs to Quartz vein type gold-bearing deposit, the main gold minerals are natural gold and silver-gold minerals, there are a few metallic minerals such as pyrite, the main gangue minerals are quartz, feldspar, sericite and so on, the average size of gold minerals is 62.37 microns, the particle size of the cloth is coarse. As a kind of gravity separation equipment, Nelson concentrator is more and more favored by mines because of its high enrichment ratio, high recovery rate and extremely friendly to environment, the experimental research of Nelson concentrator was carried out, and the gold concentrate with gold grade of 385.06 G / T and recovery of 62.21% was obtained.

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Multi-source Mechanical Signal Analysis Based on Linear Latent Structure Model for Mill Load Parameter Forecasting

Multi-source Mechanical Signal Analysis Based on Linear Late...

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第30届中国控制与决策会议

作者： Jian Tang Zhuo Liu Xiaoli Li Gang Yu Jianjun Zhao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System State Key Laboratory of Synthetical Automation for Process Industries Northeaster University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Accurate detect mill load inside the heavy rotating ball mill is one of the key factors for realizing operation optimization and control of the mineral grinding process. By using multi-source mechanical signals, such as vibration and acoustical signals at different location of the ball mill system, to construct mill load parameter forecasting(MLPF) model has been a hot and focus recently. However, a few researches about the relative accuracy and reliability contribution ratio of these different multi-source signals are addressed. It is necessary to select the suitable mechanical signal at different industrial applications. Aim to this problem, multi-source mechanical signal analysis based on linear latent structure MLPF model is studied in this paper. At first, Fast Fourier transformer(FFT) is used to obtain mechanical frequency spectrum of the time domain vibration and acoustic signals with characteristics of high dimension and strong co-linearity. Then, project to latent structure(PLS) models are build based on these spectra data. Finally, a new defined metric is used to estimate relative accuracy and reliability contribution ratio of these multi-source signals based on generalization performance and structure parameter of different prediction model. Experiments based on a laboratory-scale ball mill are used to valid the proposed method.

关键词： Mechanical frequency spectrum ball mill mill load parameter forecasting latent structure model

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An Energy Consumption Prediction LSTM Model of metallurgy Enterprises

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IOP Conference Series: Earth and Environmental Science 2020年第1期495卷

作者： Xueying Wang Zhuchao Yu Pengfei Xi Gaixia Chu Shiyang Lai Jing Li Yuanzheng Zhang School of Business Administration Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Qinghai XKXX Information Technology Co. LTD Xining China College of Medicine and Biological Information Engineering Northeastern University Shenyang China

Aiming at the characteristics of multi-dimensional production data, complicated sources and diverse data structures of metallurgy enterprises, it is of great significance to study how to use energy management-related data to predict metallurgy enterprises' energy consumption. Using with an accurate measurement method, the enterprises can not only reduce the cost of energy consumption but also develop economic efficiency in producing for metallurgy enterprises, which results in low energy use efficiency, high enterprise cost, and weak scalability. In this paper, we establish an LSTM model to achieve energy consumption prediction for metallurgy enterprises by optimizing the model's parameters using a grid search algorithm. We also compare our model's prediction results with other mainstream machine learning algorithms, i.e., MARS and SVM through indexes such as MSE, RMSE, MAPE, and MRAE to evaluate the prediction effect of the learning algorithm. According to our simulation, LSTM performs best in the task of energy consumption prediction.

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On-line Monitoring of Batch processes Using Additive Kernel Partial Least Square

On-line Monitoring of Batch Processes Using Additive Kernel ...

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2017中国智能自动化会议(CIAC 2017)

作者： Ziang Ma Huangang Wang Junwu Zhou Department of Automation Tsinghua University State Key Laboratory of Process Automation in Mining and Metallurgy

1 Introduction Batch processes are widely used in modern industry like biochemical,foods and medicines [3].In Batch process,raw materials are added in batches and the whole process can be divided into a number of stag... 详细信息

关键词： On-line Monitoring of Batch processes Using Additive Kernel Partial Least Square Gaussian

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Active Learning Based Support Vector Data Description for Large Data Set Novelty Detection

Active Learning Based Support Vector Data Description for La...

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2017中国智能自动化会议(CIAC 2017)

作者： Lili Yin Huangang Wang Wenhui Fan Qingkai Wang Department of Automation Tsinghua University State Key Laboratory of Process Automation in Mining and Metallurgy

1 Introduction Novelty detection is a popular problem in industry,network intrusion detection,disease diagnosis,*** can be seen as "one-class classification".Most of current novelty detection methods are ***... 详细信息

关键词： Lagrange Vector Active Learning Based Support Vector Data Description for Large Data Set Novelty Detection

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Data-driven Modeling and Control of Blast Furnace Ironmaking process

Data-driven Modeling and Control of Blast Furnace Ironmaking...

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第27届中国过程控制会议（CPCC2016）

作者： Heda Song Ping Zhou Hong Wang Tian-You Chai State Key Laboratory of Synthetical Automation for Process Industries Northeastern University State Key Laboratory of Process Automation in Mining & Metallurgy Control System Center Manchester University

Modeling and control of blast furnace ironmaking process have always been a dilemma due to the crucial environment and complex physical-chemical reactions in *** the most essential quality parameters of ironmaking pro... 详细信息

关键词： Data-driven Modeling and Control of Blast Furnace Ironmaking process

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